Fluid transport properties under confined conditions

This paper was presented at the 4th Micro and Nano Flows Conference (MNF2014), which was held at University College, London, UK. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute, ASME Press, LCN London Centre for Nanotechnology, UCL University College London, UCL Engineering, the International NanoScience Community, www.nanopaprika.eu.The problem of adequate description of transport processes of fluids in confined conditions is solved using methods of nonequilibrium statistical mechanics. The «fluid–channel wall» system is regarded as a two-phase medium, in which each phase has a particular velocity and temperature. The obtained results show that the transfer equations describing transport processes in confined spaces should contain not only the stress tensor and the heat flux vector, but also the interfacial forces responsible for the transfer of momentum and heat due to the interaction with the wall surfaces. The stress tensor and the heat flux vector fluid can be expressed in terms of the effective viscosity and thermal conductivity. However, the constitutive relations contain additive terms that correspond to the fluid–surface interactions. Thus, not only do the fluid transport coefficients in nanochannels differ from the bulk transport coefficients, but also they are not determined only by the parameters of the fluid

Oxidative Stress Detection With Escherichia Coli Harboring A katG\u27::lux Fusion

A plasmid containing a transcriptional fusion of the Escherichia coli katG promoter to a truncated Vibrio fischeri lux operon (luxCDABE) was constructed. An E. coli strain bearing this plasmid (strain DPD2511) exhibited low basal levels of luminescence, which increased up to 1,000-fold in the presence of hydrogen peroxide, organic peroxides, redox-cycling agents (methyl viologen and menadione), a hydrogen peroxide-producing enzyme system (xanthine and xanthine oxidase), and cigarette smoke. An oxyR deletion abolished hydrogen peroxide-dependent induction, confirming that oxyR controlled katG\u27::lux luminescence. Light emission was also induced by ethanol by an unexplained mechanism. A marked synergistic response was observed when cells were exposed to both ethanol and hydrogen peroxide; the level of luminescence measured in the presence of both inducers was much higher than the sum of the level of luminescence observed with ethanol and the level of luminescence observed with hydrogen peroxide. It is suggested that this construction or similar constructions may be used as a tool for assaying oxidant and antioxidant properties of chemicals, as a biosensor for environmental monitoring and as a tool for studying cellular responses to oxidative hazards

Detection Of DNA Damage By Use Of Escherichia Coli Carrying recA\u27::lux, uvrA\u27::lux, And alkA\u27::lux Reporter Plasmids

Plasmids were constructed in which DNA damage-inducible promoters recA, uvrA, and alkA from Escherichia coli were fused to the Vibrio fischeri luxCDABE operon. Introduction of these plasmids into E. coli allowed the detection of a dose-dependent response to DNA-damaging agents, such as mitomycin and UV irradiation. Bioluminescence was measured in real time over extended periods. The fusion of the recA promoter to luxCDABE showed the most dramatic and sensitive responses. lexA dependence of the bioluminescent SOS response was demonstrated, confirming that this biosensor\u27s reports were transmitted by the expected regulatory circuitry. Comparisons were made between luxCDABE and lacZ fusions to each promoter. It is suggested that the lux biosensors may have use in monitoring chemical, physical, and genotoxic agents as well as in further characterizing the mechanisms of DNA repair

Nonparametric Regression on a Graph

The 'Signal plus Noise' model for nonparametric regression can be extended to the case of observations taken at the vertices of a graph. This model includes many familiar regression problems. This article discusses the use of the edges of a graph to measure roughness in penalized regression. Distance between estimate and observation is measured at every vertex in the L2 norm, and roughness is penalized on every edge in the L1 norm. Thus the ideas of total variation penalization can be extended to a graph. The resulting minimization problem presents special computational challenges, so we describe a new and fast algorithm and demonstrate its use with examples. The examples include image analysis, a simulation applicable to discrete spatial variation, and classification. In our examples, penalized regression improves upon kernel smoothing in terms of identifying local extreme values on planar graphs. In all examples we use fully automatic procedures for setting the smoothing parameters. Supplemental materials are available online. © 2011 American Statistical Association

Celebrating Stephen Robertson's retirement

Stephen Robertson retired from the Microsoft Research Lab in Cambridge during the summer of 2013 after a long career as one of the most influential, well-liked and eminent researchers in Information Retrieval throughout the world

РОД GYPSOPHILA (CARYOPHYLLACEAE) В АЛТАЙСКОЙ ГОРНОЙ СТРАНЕ

Diversity, synonymy and distribution of species of Gypsophila L. in Altai Mountain Country is revised. Original key to the species determination is presented. The synopsis of Gypsophila includes 10 species from 3 subgenera and 5 sections. The name G. desertorum is typified.Приведен видовой состав, распространение, а также синонимика для видов рода Gypsophila L., произрастающих на территории Алтайской горной страны. Составлен оригинальный ключ для определения видов и конспект рода Gypsophila, включающий 10 видов, относящихся к 3 подродам и 5 секциям. Типифицировано название G. desertorum

Preferred strategies for secondary infrainguinal bypass: Lessons learned from 300 consecutive reoperations

AbstractPurpose: To determine the optimal surgical strategies in reoperative infrainguinal bypass, we reviewed our results in 300 consecutive secondary bypasses in 251 patients operated on between Jan. 1, 1975, and Nov. 1, 1993.Methods: There were 168 men (67%) and 83 women (33%), with a mean age of 64.8 years and a typical distribution of risk factors including smoking (76.4%), diabetes (33.7%), and coronary artery disease (47.1%). The indications for surgery were limb-threatening ischemia in 83.5% and severe claudication in 16.5% of patients. The majority of conduits (n = 213) were autogenous vein and were composed of a single segment of greater saphenous vein in 121 bypasses (57%) and various alternative veins including composite, arm, and lesser saphenous vein in 92 bypasses (43%). Prosthetic conduits included 69 polytetrafluoroethylene, 16 umbilical vein, and two Dacron grafts.Results: There was one perioperative death (0.3%) and a 25% total morbidity rate including a 1.7% myocardial infarction rate. There was a 28.6% early (<30 days) graft failure and 10.7% early amputation rate for prosthetic bypass grafts compared with 13.6% early graft failure and 5.6% early amputation rates for vein grafts. Autogenous vein bypasses had higher 5-year secondary patency rates than had prosthetic grafts (51.5% ± 4.6% vs 27.4% ± 6.1%, p < 0.001). Results with autogenous vein bypass improved significantly from the 1975 to 1984 to the 1985 to 1993 interval with 5-year secondary patency rates increasing from 38.3% ± 6.9% to 59.1% ± 5.8% (p = 0.017) and 5-year limb-salvage rates increasing from 40.4% ± 7.6% to 72.4% ± 6.6% (p < 0.001). Vein grafts to the popliteal and tibial outflow levels had equivalent long-term results. Vein grafts completed for claudication demonstrated results superior to those for limb salvage, with a 5-year secondary patency rate of 75.8% ± 8.1% versus 52.3% ± 7.9% (p = 0.048). Secondary autogenous vein bypass grafting performed after early primary graft failure (< 3 months) did particularly poorly, with only a 27.2% ± 7.7% 4-year secondary patency rate. Greater saphenous veins tended to perform better than alternative vein bypasses, with a 5-year secondary patency rate of 68.5% ± 6.0% compared with 48.3% ± 10.5% (p = 0.09) and a 5-year limb-salvage rate of 77.8% ± 7.4% versus 54.2% ± 11.8% (p = 0.046).Conclusions: When patients suffer a recurrence of limb-threatening ischemia at the time of infrainguinal graft failure, aggressive attempts at secondary revascularization with autogenous vein are warranted based on the low surgical morbidity and mortality rates and the improved patency and limb salvage rates that are currently attainable. (J VASC SURG 1995;21:282-95.

An integrated biochemical prediction model of all-cause mortality in patients undergoing lower extremity bypass surgery for advanced peripheral artery disease

BackgroundPatients with advanced peripheral artery disease (PAD) have a high prevalence of cardiovascular (CV) risk factors and shortened life expectancy. However, CV risk factors poorly predict midterm (<5 years) mortality in this population. This study tested the hypothesis that baseline biochemical parameters would add clinically meaningful predictive information in patients undergoing lower extremity bypass operations.MethodsThis was a prospective cohort study of patients with clinically advanced PAD undergoing lower extremity bypass surgery. The Cox proportional hazard model was used to assess the main outcome of all-cause mortality. A clinical model was constructed with known CV risk factors, and the incremental value of the addition of clinical chemistry, lipid assessment, and a panel of 11 inflammatory parameters was investigated using the C statistic, the integrated discrimination improvement index, and Akaike information criterion.ResultsThe study monitored 225 patients for a median of 893 days (interquartile range, 539-1315 days). In this study, 50 patients (22.22%) died during the follow-up period. By life-table analysis (expressed as percent surviving ± standard error), survival at 1, 2, 3, 4, and 5 years, respectively, was 90.5% ± 1.9%, 83.4% ± 2.5%, 77.5% ± 3.1%, 71.0% ± 3.8%, and 65.3% ± 6.5%. Compared with survivors, decedents were older, diabetic, had extant coronary artery disease, and were more likely to present with critical limb ischemia as their indication for bypass surgery (P < .05). After adjustment for the above, clinical chemistry and inflammatory parameters significant (hazard ratio [95% confidence interval]) for all-cause mortality were albumin (0.43 [0.26-0.71]; P = .001), estimated glomerular filtration rate (0.98 [0.97-0.99]; P = .023), high-sensitivity C-reactive protein (hsCRP; 3.21 [1.21-8.55]; P = .019), and soluble vascular cell adhesion molecule (1.74 [1.04-2.91]; P = .034). Of the inflammatory molecules investigated, hsCRP proved most robust and representative of the integrated inflammatory response. Albumin, eGFR, and hsCRP improved the C statistic and integrated discrimination improvement index beyond that of the clinical model and produced a final C statistic of 0.82.ConclusionsA risk prediction model including traditional risk factors and parameters of inflammation, renal function, and nutrition had excellent discriminatory ability in predicting all-cause mortality in patients with clinically advanced PAD undergoing bypass surgery

Manifold Elastic Net: A Unified Framework for Sparse Dimension Reduction

It is difficult to find the optimal sparse solution of a manifold learning based dimensionality reduction algorithm. The lasso or the elastic net penalized manifold learning based dimensionality reduction is not directly a lasso penalized least square problem and thus the least angle regression (LARS) (Efron et al. \cite{LARS}), one of the most popular algorithms in sparse learning, cannot be applied. Therefore, most current approaches take indirect ways or have strict settings, which can be inconvenient for applications. In this paper, we proposed the manifold elastic net or MEN for short. MEN incorporates the merits of both the manifold learning based dimensionality reduction and the sparse learning based dimensionality reduction. By using a series of equivalent transformations, we show MEN is equivalent to the lasso penalized least square problem and thus LARS is adopted to obtain the optimal sparse solution of MEN. In particular, MEN has the following advantages for subsequent classification: 1) the local geometry of samples is well preserved for low dimensional data representation, 2) both the margin maximization and the classification error minimization are considered for sparse projection calculation, 3) the projection matrix of MEN improves the parsimony in computation, 4) the elastic net penalty reduces the over-fitting problem, and 5) the projection matrix of MEN can be interpreted psychologically and physiologically. Experimental evidence on face recognition over various popular datasets suggests that MEN is superior to top level dimensionality reduction algorithms.Comment: 33 pages, 12 figure